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GIAC Machine Learning Engineer Sample Questions:

1. How does 'L1 regularization' differ from 'L2 regularization' in machine learning?
Response:

A) L1 is for neural networks; L2 is for decision trees
B) L1 optimizes the learning rate; L2 optimizes the batch size
C) L1 adds the absolute value of coefficients; L2 adds the square of coefficients
D) L1 is used for regression; L2 is used for classification

2. Why is feature scaling important in machine learning?
Response:

A) It increases the number of features
B) It makes the model training process faster
C) It ensures that different features contribute equally to the model training
D) It helps in handling missing data

3. Which statistic is used to measure the relationship between two variables?
Response:

A) Median
B) Standard deviation
C) Mean
D) Correlation coefficient

4. Which of the following describes a decision tree in supervised learning?
Response:

A) An algorithm that groups data points into clusters without labels
B) A neural network with multiple hidden layers
C) A model that makes decisions by splitting data into branches based on feature values
D) A tree-like structure used for clustering data points

5. What is the main advantage of using a 'Random Forest' over a 'Decision Tree'?
Response:

A) Simpler interpretation and understanding
B) Reduced risk of overfitting
C) Faster computation
D) Higher flexibility in model tuning

Solutions: